Editing Embryo transfer (section)

== Embryo selection ==
{{Further|Embryo quality}}
Laboratories have developed grading methods to judge oocyte and [[embryo]] quality. In order to optimise [[pregnancy rate]]s, there is significant evidence that a morphological scoring system is the best strategy for the selection of embryos.<ref name=Rebmann>{{cite journal | vauthors = Rebmann V, Switala M, Eue I, Grosse-Wilde H | title = Soluble HLA-G is an independent factor for the prediction of pregnancy outcome after ART: a German multi-centre study | journal = Human Reproduction | volume = 25 | issue = 7 | pages = 1691–8 | date = July 2010 | pmid = 20488801 | doi = 10.1093/humrep/deq120 | doi-access = free }}</ref> Since 2009 where the first [[time-lapse microscopy]] system for IVF was approved for clinical use, morphokinetic scoring systems has shown to improve to [[pregnancy rate]]s further.<ref name="Meseguer">{{cite journal | vauthors = Meseguer M, Rubio I, Cruz M, Basile N, Marcos J, Requena A | title = Embryo incubation and selection in a time-lapse monitoring system improves pregnancy outcome compared with a standard incubator: a retrospective cohort study | journal = Fertility and Sterility | volume = 98 | issue = 6 | pages = 1481–9.e10 | date = December 2012 | pmid = 22975113 | doi = 10.1016/j.fertnstert.2012.08.016 | doi-access = free }}</ref> However, when all different types of [[time-lapse embryo imaging]] devices, with or without morphokinetic scoring systems, are compared against conventional embryo assessment for IVF, there is insufficient evidence of a difference in live-birth, pregnancy, stillbirth or miscarriage to choose between them.<ref>{{cite journal |last1=Armstrong |first1=S |last2=Bhide |first2=P |last3=Jordan |first3=V |last4=Pacey |first4=A |last5=Marjoribanks |first5=J |last6=Farquhar |first6=C |title=Time-lapse systems for embryo incubation and assessment in assisted reproduction. |journal=The Cochrane Database of Systematic Reviews |date=29 May 2019 |volume=5 |issue=5 |pages=CD011320 |doi=10.1002/14651858.CD011320.pub4 |pmid=31140578|pmc=6539473 }}</ref> A small prospectively randomized study in 2016 reported poorer embryo quality and more staff time in an automated [[time-lapse embryo imaging]] device compared to conventional embryology.<ref>{{cite journal | vauthors = Wu YG, Lazzaroni-Tealdi E, Wang Q, Zhang L, Barad DH, Kushnir VA, Darmon SK, Albertini DF, Gleicher N|author-link9=Norbert Gleicher | title = Different effectiveness of closed embryo culture system with time-lapse imaging (EmbryoScope(TM)) in comparison to standard manual embryology in good and poor prognosis patients: a prospectively randomized pilot study | journal = Reproductive Biology and Endocrinology | volume = 14 | issue = 1 | pages = 49 | date = August 2016 | pmid = 27553622 | pmc = 4995783 | doi = 10.1186/s12958-016-0181-x |doi-access=free }}</ref> Active efforts to develop a more accurate embryo selection analysis based on Artificial Intelligence and Deep Learning are underway. [[Embryo Ranking Intelligent Classification Algorithm]] (ERICA),<ref>{{Cite web | url=https://embryoranking.com/ |title = ERICA Embryo Ranking &#124; Artificial Intelligence for Assisted Reproduction}}</ref> is a clear example.  This Deep Learning software substitutes manual classifications with a ranking system based on an individual embryo's predicted genetic status in a non-invasive fashion.<ref>{{Cite journal | doi=10.1016/j.fertnstert.2019.07.715| title=Artificial vision and machine learning designed to predict PGT-A results| journal=Fertility and Sterility| volume=112| issue=3| pages=e231| year=2019| last1=Chavez-Badiola| first1=Alejandro| last2=Flores-Saiffe Farias| first2=Adolfo| last3=Mendizabal-Ruiz| first3=Gerardo| last4=Drakeley| first4=Andrew J.| last5=Garcia-Sánchez| first5=Rodolfo| last6=Zhang| first6=John J.| doi-access=free}}</ref> Studies on this area are still pending and current feasibility studies support its potential.<ref>{{cite journal |last1=Chavez-Badiola |first1=Alejandro |last2=Flores-Saiffe Farias |first2=Adolfo |last3=Mendizabal-Ruiz |first3=Gerardo |last4=Garcia-Sanchez |first4=Rodolfo |last5=Drakeley |first5=Andrew J. |last6=Garcia-Sandoval |first6=Juan Paulo |title=Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning |journal=Scientific Reports |date=10 March 2020 |volume=10 |issue=1 |pages=4394 |doi=10.1038/s41598-020-61357-9 |pmid=32157183 |pmc=7064494 |bibcode=2020NatSR..10.4394C }}</ref>